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Related Experiment Videos

Input-output nonlinearities and time delays increase tracking errors in hand grasp neuroprostheses

M M Adamczyk1, P E Crago

  • 1Department of Orthopaedic Surgery, MetroHealth Medical Center, Cleveland, OH 44109, USA.

IEEE Transactions on Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
|December 1, 1996
PubMed
Summary

This study found that nonlinearities and time delays in functional neuromuscular stimulation (FNS) hand grasp neuroprostheses increase tracking errors. Linear systems with no delays improve control accuracy for better neuroprosthetic hand function.

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Area of Science:

  • Biomedical Engineering
  • Neuroprosthetics
  • Rehabilitation Technology

Background:

  • Functional neuromuscular stimulation (FNS) enables grasping for individuals with paralysis.
  • Controlling FNS hand grasp neuroprostheses presents challenges due to system complexities.
  • Understanding the impact of system properties on control performance is crucial for improving neuroprosthetic devices.

Purpose of the Study:

  • To investigate how input-output relationships, specifically nonlinearities and time delays, affect the performance of FNS hand grasp neuroprostheses.
  • To compare the performance of simulated and real FNS hand grasp neuroprostheses under varying conditions.
  • To identify key factors influencing the accuracy of FNS hand grasp control.

Main Methods:

  • Tracking tasks were employed using both simulated and real FNS hand grasp neuroprostheses.

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  • Able-bodied subjects used simulated systems, while neuroprosthesis users operated real systems.
  • Performance was evaluated based on tracking error across different bandwidths, nonlinearities, and time delays, with and without closed-loop control.

    • Main Results:

    • Tracking error increased proportionally with greater input-output nonlinearity in both simulated and real FNS systems.
    • Increased target bandwidth and neuroprosthetic delays also negatively impacted tracking performance.
    • Closed-loop control was evaluated but the primary focus was on inherent system properties.

    Conclusions:

    • FNS hand grasp neuroprostheses with linear input-output properties and minimal time delays are controlled more accurately.
    • Minimizing nonlinearity and delays in FNS system design can enhance user control and functional outcomes.
    • These findings provide valuable insights for the development of more effective neuroprosthetic hand grasps.